As is known in the art, cardiac perfusion magnetic resonance imaging (MRI) provides a valuable tool for diagnosing cardiovascular diseases in an accurate manner, see, for example, an article by E. Nagel et al. entitled “Magnetic resonance perfusion measurements for the noninvasive detection of coronary artery disease”, published in Circulation, 108(4):432-437, July, 2003. In a cardiac MR perfusion study, the heart is scanned along short-axis slices repeatedly at the same phase of the cardiac cycle through electrocardiographic (ECG) gating, following a bolus injection of a contrast agent. Because it takes a few minutes to acquire dynamic gated images and to obtain reliable information about the perfusion, patient breathing during image acquisition often causes large variations in the position of the left ventricle (LV) at different frames of the acquired cardiac MR image sequence. A change in the position of the diaphragm results in both in-plane and through-plane motion of the heart relative to the MR acquisition plane. At the same time, the shape of the heart might change over the course of contrast enhancement due to the noise in the ECG gating signal and changes in heart rate. Therefore, nonrigid, i.e., flexible, registration must be performed on time-series images to account for both local deformation and global translation so that appropriate pixel correspondence can be established between different frames.
A goal of a cardiac perfusion study is to evaluate the perfusion within the ventricle muscle. Dead or ischemic tissue will show a perfusion deficit. An important challenge in perfusion analysis is recovering a consistent definition of the LV boundaries so that a signal time curve reflects the perfusion of a single local tissue sample. Contours defined too far into the blood pool or around papillary muscles that extend into the blood pool will result in a contaminated signal.
Several methods have been proposed for automatic registration of cardiac MR perfusion images. L. M. Bidaut and J.-P. Vallee proposed in an article entitled “Automated registration of dynamic MR images for the quantification of myocardial perfusion”, published in the Journal of Magnetic Resonance Imaging, 13:648-655, 2001, a registration method that minimizes intrinsic intensity differences inside a manually drawn mask between each image and a reference image coupled to a two-dimensional rigid body correction. Another intensity-based multi-resolution registration algorithm was proposed by C. Dornier et al. in an article entitled “Improvement in the quantification of myocardial perfusion using an automatic spline-based registration algorithm”, published in Journal of Magnetic Resonance Imaging, 18:160-168, 2003. These intensity-based algorithms do not take into account the fact that the signal intensity at the same physical location changes rapidly across the MR image sequence due to the wash-in and wash-out of the contrast agent. To address this problem, R. Bansal and G. Funka-Lea. proposed in an article entitled “Integrated image registration for cardiac MR perfusion data”, published in MICCAI(1) 2002:659-666, an image registration algorithm that utilizes the maximization of mutual information. However, it only estimates the integer pixel translation of the two manually drawn contours. Recently, Y. Sun, M.-P. Jolly, and J. M. F. Moura in an article entitled “Contrast-invariant Registration of Cardiac and Renal MR Perfusion Images”, published in MICCAI, France, September 2004, introduced a contrast-invariant similarity metric and proposed a common framework to perform affine registration on both cardiac and renal MR perfusion images.